... problem is press coverage lags the actual event by days or weeks, so everyone reaches out at the same time once it's on TechCrunch. I built Funding Signals to fix that: it watches the SEC's daily EDGAR Form D index directly (the raw filing, not the press release), scores each raise 0-100, and enriches it with the company's domain and a contact email. Free tier, no card needed: fundingsignals.net Would love feedback, especially from anyone doing ...

... would not be shipping a production database as a solo non-backend developer without it. SEC EDGAR - sounds odd but the free public filing database is the core data source for everything I build. Genuinely underrated. Vercel - zero ops, instant deploys. I could not maintain infrastructure and build product simultaneously without it. Resend - transactional email that just works. I wasted two days on alternatives

... Financial figures come directly from the SEC's XBRL structured data feeds. The AI reads the text and flags red flags. It never touches a number. Every claim links back to the verbatim paragraph in the original EDGAR filing. One click to the source. The result is Sharpread. Forensic SEC filing analysis where the AI reads the filings but never generates the numbers. If you invest in individual stocks and actually read 10-Ks, it might be useful. sharpread.ai What ...

... wanted to give retail investors the kind of deep financial analysis that normally sits behind $20-40/mo paywalls. The catch? The clean data doesn't exist for free. You have to pull it from raw SEC EDGAR filings, and XBRL parsing is an absolute nightmare. Every company tags their financials slightly differently. Edge cases everywhere. I lost count of the nights spent chasing why one company's revenue was off by a rounding quirk or a switched tag. Getting ...

... first. I built Sharpread, a forensic SEC filing analysis tool, on a standard in-process rate limiter for managing requests to the SEC EDGAR database. It worked perfectly in development and in early production. Then I hit a wall. Vercel's serverless architecture spins up multiple Lambda instances concurrently. Each instance had its own rate limiter. They had no idea the others existed. Under any real traffic spike, the instances collectively hammered EDGAR

... debug. Every action call needs full params + response logged so you can understand exactly what the AI did and why. Curious how others are thinking about this: How are you deciding which actions to expose to your AI vs. which ones stay human-controlled? Are you prompting heavily to guide action selection, or letting the model figure it out? What's broken for you that current tool-use implementations don't solve? We just shipped AI Actions on Amarsia with ... ... source sentence, it drops the claim entirely. It does not approximate, summarise, or fill in the gaps. Your point on logging is exactly right. Every Sharpread analysis links each claim back to the highlighted paragraph in the original EDGAR

... think about most. I built Sharpread as a solo founder and the single rule I gave myself early was - nothing gets demoed that is not live in production. Every red flag in the demo links to a real SEC EDGAR paragraph. Every financial metric comes from live XBRL data. If it breaks in production, it does not go in the demo. The discipline that enforces this for me is that the demo is the product. There is no separate demo ... ... team moves to an "okay, we figured it out" mode. Suddenly, the comfort zone exists . Now the cost of staying in it is lower than the cost of leaving it. But the market doesn't stop changing. Competitors

... money from the subscription, and the other month the user wouldn't use the full value of the app. So the usage-based is a perfect balance. For me, usage based sees a lot more trying-it-out users, versus standard SaaS. So theres more conversions. Comment from Serdar D(@serdarships): Dansaki's answer is the one I would build on, and I would add one turn to it. Visibility and control matter more than the label on the plan ... ... user heavily uses AI-powered steps, agents, or transformations, credits are consumed faster because real token and compute costs are involved. From the buyer s perspective, pricing stays consolidated. They do not have to reason separately about SaaS fees versus